@inproceedings{tang2011learning,
author = {Tang, Huixuan and Joshi, Neel and Kapoor, Ashish},
title = {Learning a Blind Measure of Perceptual Image Quality},
booktitle = {CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition},
year = {2011},
month = {June},
abstract = {It is often desirable to evaluate an image based on its quality. For many computer vision applications, a perceptually meaningful measure is the most relevant for evaluation; however, most commonly used measure do not map well to human judgements of image quality. A further complication of many existing image measure is that they require a reference image, which is often not available in practice. In this paper, we present a “blind” image quality measure, where potentially neither the groundtruth image nor the degradation process are known. Our method uses a set of novel low-level image features in a machine learning framework to learn a mapping from these features to subjective image quality scores. The image quality features stem from natural image measure and texture statistics. Experiments on a standard image quality benchmark dataset shows that our method outperforms the current state of art.},
publisher = {IEEE},
url = {http://approjects.co.za/?big=en-us/research/publication/learning-a-blind-measure-of-perceptual-image-quality/},
pages = {305-312},
}